Computational Materials Science Research Articles

Framework for the Generation of 3D Fiber Composite Structures from 2D Observations

AbstractSynthetic generation of realistic materials for testing of process–structure–property (PSP) relationships in computational materials science has gained significant traction over the past two decades. Generation tools continue to lag in some aspects of realism, leading to uncertainty and errors in simulating material response. The experimental collection of information to guide generation of 3D synthetic structures remains costly, time consuming, and challenging. These challenges are compounded by limitations of stereology, which permits estimation of 3D microstructural statistics from 2D observations under restrictive assumptions on constituent morphologies and size distributions and can be difficult to apply to microstructure metrics like constituent orientation distribution and clustering. This work seeks to overcome these challenges by introducing a framework for learning probable 3D microstructure statistics by minimizing the loss determined by matching statistics obtained from 2D observations via synthetic microstructure generation software (e.g. Dream.3D) and stereological principles. This framework is applied to short carbon fiber composite structures printed from an additive manufacturing process, Direct Ink Writing, with the hope that the framework could generalize to other particulate structures as well as allow for simulation and design of materials structure.

Interatomic Interaction Models for Magnetic Materials: Recent Advances

Interatomic Interaction Models for Magnetic Materials: Recent Advances

Micro-macro consistency in multiscale modeling: Score-based model assisted sampling of fast/slow dynamical systems.

A valuable step in the modeling of multiscale dynamical systems in fields such as computational chemistry, biology, and materials science is the representative sampling of the phase space over long time scales of interest; this task is not, however, without challenges. For example, the long term behavior of a system with many degrees of freedom often cannot be efficiently computationally explored by direct dynamical simulation; such systems can often become trapped in local free energy minima. In the study of physics-based multi-time-scale dynamical systems, techniques have been developed for enhancing sampling in order to accelerate exploration beyond free energy barriers. On the other hand, in the field of machine learning (ML), a generic goal of generative models is to sample from a target density, after training on empirical samples from this density. Score-based generative models (SGMs) have demonstrated state-of-the-art capabilities in generating plausible data from target training distributions. Conditional implementations of such generative models have been shown to exhibit significant parallels with long-established-and physics-based-solutions to enhanced sampling. These physics-based methods can then be enhanced through coupling with the ML generative models, complementing the strengths and mitigating the weaknesses of each technique. In this work, we show that SGMs can be used in such a coupling framework to improve sampling in multiscale dynamical systems.

The materials design ontology

In the materials design domain, much of the data from materials calculations is stored in different heterogeneous databases with different data and access models. Therefore, accessing and integrating data from different sources is challenging. As ontology-based access and integration alleviates these issues, in this paper we address data access and interoperability for computational materials databases by developing the Materials Design Ontology. This ontology is inspired by and guided by the OPTIMADE effort that aims to make materials databases interoperable and includes many of the data providers in computational materials science. In this paper, first, we describe the development and the content of the Materials Design Ontology. Then, we use a topic model-based approach to propose additional candidate concepts for the ontology. Finally, we show the use of the Materials Design Ontology by a proof-of-concept implementation of a data access and integration system for materials databases based on the ontology.11 This paper is an extension of (In The Semantic Web – ISWC 2020 – 19th International Semantic Web Conference, Proceedings, Part II (2000) 212–227 Springer) with results from (In ESWC Workshop on Domain Ontologies for Research Data Management in Industry Commons of Materials and Manufacturing 2021 1–11) and currently unpublished results regarding an application using the ontology.

From Local Atomic Environments to Molecular Information Entropy.

The similarity of local atomic environments is an important concept in many machine learning techniques, which find applications in computational chemistry and material science. Here, we present and discuss a connection between the information entropy and the similarity matrix of a molecule. The resulting entropy can be used as a measure of the complexity of a molecule. Exemplarily, we introduce and evaluate two specific choices for defining the similarity: one is based on a SMILES representation of local substructures, and the other is based on the SOAP kernel. By tuning the sensitivity of the latter, we can achieve good agreement between the respective entropies. Finally, we consider the entropy of two molecules in a mixture. The gain of entropy due to the mixing can be used as a similarity measure of the molecules. We compare this measure to the average and best-match kernel. The results indicate a connection between the different approaches and demonstrate the usefulness and broad applicability of the similarity-based entropy approach.

Self‐healing anti‐corrosion coatings: A mechanism study using computational materials science

AbstractSelf‐healing coatings possess the remarkable ability to autonomously mend damages and restore their corrosion protection capabilities, which are compromised due to environmental influences or external forces. The progression of computational materials science has significantly bolstered theoretical explorations into self‐healing anti‐corrosion coatings. Such inquiries enhance our grasp of self‐healing mechanisms, refine coating efficacy, and curtail the expenditures associated with empirical approaches. The research advancements in computational materials science for self‐healing coatings are reviewed here. It delineates the theoretical elucidation of self‐healing mechanisms through the lens of molecular dynamics, density functional theory, Monte Carlo simulations, and finite element analysis. The computational exploration of intrinsic self‐healing coatings primarily involves dynamic reversible covalent bonds, dynamic reversible non‐covalent bonds, and shape memory effect; whereas, extrinsic self‐healing coatings concentrate on the modalities of film‐forming substances and the adsorption behaviours of corrosion inhibitors. These simulation techniques not only shed light on the self‐healing processes at a microscopic scale but also enable the prediction and enhancement of the macroscopic performance attributes of the coatings in practical applications. The merits and constraints of these computational simulation methodologies when applied to self‐healing coatings are critically assessed. Looking ahead, efforts will be directed towards a more profound amalgamation of computational simulations, empirical investigations, and artificial intelligence. This will pave the way for creating a comprehensive database cataloguing the self‐healing properties of coatings, thereby streamlining material selection and design with greater efficiency.

Investigation of soliton solutions to the Peyrard-Bishop-Deoxyribo-Nucleic-Acid dynamic model with beta-derivative

This study purposes to extract some fractional analytical solutions of the Peyrard-Bishop-Deoxyribo-Nucleic-Acid ([Formula: see text]-PBDNA) dynamic model with the beta-derivative by the unified Riccati equation expansion method (UREEM). Furthermore, we examine the role of various parameters of the fractional model on the soliton dynamic. The research focuses on computational biophysics and materials science, examining the impact of various parameters on the fractional model. This paper contributes to understanding soliton solutions and the [Formula: see text]-PBDNA dynamic model, demonstrating the applicability of the UREEM method to various fractional models. Some soliton solutions of the model are successfully generated by applying the UREEM. Implementing the UREEM, we take a fractional wave transformation to convert the model into a nonlinear ordinary differential equation. So, a linear equation system is generated. After the system is solved, the soliton solutions are gained for the appropriate solution sets. Finally, 3D, 2D and contour graphs of diverse solutions are depicted at suitable values of parameters. In addition, this paper presents 3D, 2D and contour graphs of various solutions with suitable parameter values. The results are beneficial for interpreting the model in future work and confirm that UREEM is effectively applicable to diverse fractional models, coupled with a comprehensive graphical analysis of how different parameters influence these solutions.

EVALUATING THE INFLUENCE OF INCORPORATING COMPUTER SCIENCE, MATERIALS SCIENCE, AND DESIGN IN MECHANICAL AND ELECTRICAL ELECTRONICS EDUCATION THROUGH INTERDISCIPLINARY APPROACHES

The field of engineering education needs to adapt to the rapid advancements in technology and the increasing complexity of modern systems. This necessitates a shift towards more interdisciplinary approaches. In this study, we assess the impact of integrating elements from computer science, materials science, and design into traditional mechanical and electrical electronics curricula. We employ a mixed-methods approach that combines quantitative assessment of student performance with qualitative analysis of student experiences. Our goal is to examine the potential benefits and challenges of interdisciplinary learning in this particular domain. The results of our study indicate that interdisciplinary approaches can have a positive impact on students' problem-solving abilities. Furthermore, these approaches can foster creativity and better prepare students for the multifaceted demands of the industry. However, we also identified several challenges that need to be addressed in order to effectively implement interdisciplinary education. These challenges include curriculum design, resource allocation, and faculty expertise. Overall, our research contributes to the ongoing discourse on interdisciplinary education. It provides valuable insights for educators, policymakers, and industry stakeholders who are interested in cultivating a versatile and adaptable workforce. Such a workforce would be capable of addressing the complex technological challenges that arise in today's world. In conclusion, as technology continues to advance at a rapid pace, it is crucial for engineering education to embrace interdisciplinary approaches. By integrating elements from various disciplines, such as computer science, materials science, and design, we can enhance students' problem-solving abilities and prepare them for the demands of the industry. However, it is important to address the challenges associated with interdisciplinary education in order to ensure its effective implementation and maximize its benefits.

Ab initio guided atomistic modelling of nanomaterials on exascale high-performance computing platforms

The continuous development of increasingly powerful supercomputers makes theory-guided discoveries in materials and molecular sciences more achievable than ever before. On this ground, the incoming arrival of exascale supercomputers (running over 1018 floating point operations per second) is a key milestone that will tremendously increase the capabilities of high-performance computing (HPC). The deployment of these massive platforms will enable continuous improvements in the accuracy and scalability of ab initio codes for materials simulation. Moreover, the recent progress in advanced experimental synthesis and characterisation methods with atomic precision has led ab initio-based materials modelling and experimental methods to a convergence in terms of system sizes. This makes it possible to mimic full-scale systems in silico almost without the requirement of experimental inputs. This article provides a perspective on how computational materials science will be further empowered by the recent arrival of exascale HPC, going alongside a mini-review on the state-of-the-art of HPC-aided materials research. Possible challenges related to the efficient use of increasingly larger and heterogeneous platforms are commented on, highlighting the importance of the co-design cycle. Also, some illustrative examples of materials for target applications, which could be investigated in detail in the coming years based on a rational nanoscale design in a bottom-up fashion, are summarised.

DECA: Discrete Event inspired Cellular Automata for grain structure prediction in additive manufacturing

Microstructure largely dictates macroscopic material properties and is strongly affected by processing. Therefore, the simulation of microstructure evolution in response to thermal fields during processing is of significant interest within the computational materials science community. Additive manufacturing (AM) has emerged as a technique for producing complex geometries and unique microstructures. Yet, complex and rapid thermal cycles in AM pose computational challenges for existing microstructure models. This work proposes a discrete event inspired cellular automata (CA) approach, titled DECA, to accelerate simulation of grain structure evolution in AM. In contrast to conventional time-stepped CA models, this model directly solves the times capture events would take place allowing for stepping in events rather than time (a technique also found in the field of discrete-event simulation). In comparison to purely serial discrete-event models, DECA allows for temporary violation of the causality constraint, but detects and corrects these violations, leading to an emergent phenomenon dubbed causality rippling, in which previously calculated capture events are overwritten. The amount of repeated calculations, defined by the capture ratio, is taken as a measure of computational inefficiency, and the model parameters that affect this ratio are evaluated. The new DECA approach was found to be more computationally efficient than conventional time-stepped CA models while guaranteeing an accurate solution, which can only be achieved in the conventional models for vanishingly small time steps. Finally, opportunities for parallelization and scaling of the new approach are discussed.

Local-environment-guided selection of atomic structures for the development of machine-learning potentials.

Machine learning potentials (MLPs) have attracted significant attention in computational chemistry and materials science due to their high accuracy and computational efficiency. The proper selection of atomic structures is crucial for developing reliable MLPs. Insufficient or redundant atomic structures can impede the training process and potentially result in a poor quality MLP. Here, we propose a local-environment-guided screening algorithm for efficient dataset selection in MLP development. The algorithm utilizes a local environment bank to store unique local environments of atoms. The dissimilarity between a particular local environment and those stored in the bank is evaluated using the Euclidean distance. A new structure is selected only if its local environment is significantly different from those already present in the bank. Consequently, the bank is then updated with all the new local environments found in the selected structure. To demonstrate the effectiveness of our algorithm, we applied it to select structures for a Ge system and a Pd13H2 particle system. The algorithm reduced the training data size by around 80% for both without compromising the performance of the MLP models. We verified that the results were independent of the selection and ordering of the initial structures. We also compared the performance of our method with the farthest point sampling algorithm, and the results show that our algorithm is superior in both robustness and computational efficiency. Furthermore, the generated local environment bank can be continuously updated and can potentially serve as a growing database of feature local environments, aiding in efficient dataset maintenance for constructing accurate MLPs.

Decoding crystallization behavior of aluminoborosilicate glasses: From structural descriptors to Quantitative Structure – Property Relationship (QSPR) based predictive models

Successful decoding of structural descriptors controlling the crystallization in multicomponent functional glasses can pave the way for the transition from the trial-and-error approach and empirical modeling for glass/glass-ceramic composition design toward more rational and scientifically rigorous Quantitative Structure-Property Relationship (QSPR) based models. However, due to the compositional and structural complexity of multicomponent glasses and the longer time and length scales associated with nucleation, the development and validation of QSPR models are still in it's infancy. The work presented in the article is an attempt to leap forward in this pursuit by combining the strengths of experimental and computational materials science to decode the chemo-structural drivers that promote or suppress nucleation and crystal growth in alkali/alkaline-earth aluminoborosilicate glasses leading to the development of a QSPR-based model (powered by MD simulations). The results reveal the following two descriptors that govern the nucleation and crystallization of a particular aluminosilicate phase in the functional glasses: (1) degree of mixing between the SiO4 and AlO4 units, i.e., Si–O–Al linkages, and (2) difference/similarity between the short-to-intermediate range ordering in the glass structure to that of the structure of corresponding crystalline phase. Based on the established composition–structure–crystallization behavior relationships, a cluster analysis based QSPR model has been developed (and tested) to predict the propensity of nepheline (and anorthite) crystallization in the investigated glasses. The model has been tested on several compositions from the present and previous studies and has successfully predicted the crystallization propensity of all glass compositions, even in cases where previous empirical and semi-empirical models were unsuccessful.

GradDFT. A software library for machine learning enhanced density functional theory.

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange-correlation energy functionals. GradDFT employs a pioneering parametrization of exchange-correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy.

Detecting possible pairs of materials for composites using a material word co-occurrence network.

Composite materials are popular because of their high performance capabilities, but new material development is time-consuming. To accelerate this process, researchers studying material informatics, an academic discipline combining computational science and material science, have developed less time-consuming approaches for predicting possible material combinations. However, these processes remain problematic because some materials are not suited for them. The limitations of specific candidates for new composites may cause potential new material pairs to be overlooked. To solve this problem, we developed a new method to predict possible composite material pairs by considering more materials than previous techniques. We predicted possible material pairs by conducting link predictions of material word co-occurrence networks while assuming that co-occurring material word pairs in scientific papers on composites were reported as composite materials. As a result, we succeeded in predicting the co-occurrence of material words with high specificity. Nodes tended to link to many other words, generating new links in the created co-occurrence material word network; notably, the number of material words co-occurring with graphene increased rapidly. This phenomenon confirmed that graphene is an attractive composite component. We expect our method to contribute to the accelerated development of new composite materials.

High-Throughput Prediction of the Thermal and Electronic Transport Properties of Large Physical and Chemical Spaces Accelerated by Machine Learning: Charting the ZT of Binary Skutterudites.

Thermal and electronic transport properties are the keys to many technological applications of materials. Thermoelectric, TE, materials can be considered a singular case in which not only one but three different transport properties are combined to describe their performance through their TE figure of merit, ZT. Despite the availability of high-throughput experimental techniques, synthesizing, characterizing, and measuring the properties of samples with numerous variables affecting ZT are not a cost- or time-efficient approach to lead this strategy. The significance of computational materials science in discovering new TE materials has been running in parallel to the development of new frameworks and methodologies to compute the electron and thermal transport properties linked to ZT. Nevertheless, the trade-off between computational cost and accuracy has hindered the reliable prediction of TE performance for large chemical spaces. In this work, we present for the first time the combination of new ab initio methodologies to predict transport properties with machine learning and a high-throughput framework to establish a solid foundation for the accurate prediction of thermal and electron transport properties. This strategy is applied to a whole family of materials, binary skutterudites, which are well-known as good TE candidates. Following this methodology, it is possible not only to connect ZT with the experimental synthetic (carrier concentration and grain size) and operando (temperature) variables but also to understand the physical and chemical phenomena that act as driving forces in the maximization of ZT for p-type and n-type binary skutterudites.

ChatGPT in the Material Design: Selected Case Studies to Assess the Potential of ChatGPT.

The pursuit of designing smart and functional materials is of paramount importance across various domains, such as material science, engineering, chemical technology, electronics, biomedicine, energy, and numerous others. Consequently, researchers are actively involved in the development of innovative models and strategies for material design. Recent advancements in analytical tools, experimentation, and computer technology additionally enhance the material design possibilities. Notably, data-driven techniques like artificial intelligence and machine learning have achieved substantial progress in exploring various applications within material science. One such approach, ChatGPT, a large language model, holds transformative potential for addressing complex queries. In this article, we explore ChatGPT's understanding of material science by assigning some simple tasks across various subareas of computational material science. The findings indicate that while ChatGPT may make some minor errors in accomplishing general tasks, it demonstrates the capability to learn and adapt through human interactions. However, issues like output consistency, probable hidden errors, and ethical consequences should be addressed.

Open computational materials science.

Open computational materials science.

Refining DIIS algorithms for Si and GaAs solar cells: incorporation of weight regularization, conjugate gradient, and reverse automatic differentiation techniques.

Pivotal enhancements in electronic minimization algorithms, which are vital for the advancement of computational materials science, are introduced in this research. Our research is dedicated to refining the DIIS algorithm specifically for electronic structure calculations of silicon (Si) and gallium arsenide (GaAs) solar cells, aiming to enhance their efficiency and stability. We have enriched DIIS by integrating a weight regularization factor, significantly bolstering its convergence stability. This modification enhances iteration robustness and curtails the average iteration duration, thus streamlining the convergence process. Furthermore, we have incorporated the conjugate gradient (CG) algorithm to proficiently resolve symmetric positive definite residual matrices. This inclusion substantially accelerates the solution-finding process within the DIIS framework. A novel aspect of our research is the application of reverse automatic differentiation (AD), deployed in two distinct methodologies: the construction of the Jacobian matrix and direct chain rule application for gradient computation. These approaches involve sophisticated mathematical techniques that enhance computational precision and efficiency specifically for Si and GaAs solar cell materials in determining the optimal weights for residual combinations during DIIS iterations. The integration of these advanced methods into the DIIS algorithm not only augments its convergence stability but also ensures a substantial reduction in total computational time. Our findings demonstrate that the enhanced DIIS, CG-enhanced DIIS, and AD-integrated DIIS methods collectively lead to a more efficient electronic minimization process. This balance of stability and efficiency is crucial in high-performance computational materials science, particularly for complex systems analysis. The findings of this research represent a notable advancement in computational strategies for Si and GaAs solar cell materials, providing enhanced methodologies and insights that significantly improve the efficiency and stability of electronic structure calculations in these critical components of renewable energy technologies.

Exploration and Practice in Ideological and Political Education in Computational Materials Science: Cultivating Students' Comprehensive Qualities

Exploration and Practice in Ideological and Political Education in Computational Materials Science: Cultivating Students' Comprehensive Qualities

Design of a Multitask and Terrestrial Robot Challenge for Course Project of Intelligent Robots

Robotics is a highly practical, multidisciplinary, and rapidly developing field of study which lies at the intersection of computer science, automatic control, mechanical engineering, and materials science, as well as other disciplines. Designing a suitable yet challenging robotics course project is incredibly important and beneficial for teaching the subject effectively. Carrying out a practical robotics project helps students consolidate their knowledge and connect concepts across disparate areas of computer science and mechatronics engineering as they design and develop an integrated robotic system. Additionally, a practical project helps deepen a student’s understanding of how recent ground-breaking results in Artificial Intelligence (AI) can be implemented in a real robotic system. This paper provides an overview of a project for the Intelligent Robots graduate course. In the proposed course project, each team programs a small-scale humanoid robot to carry out a multi-task robot challenge on a terrestrial map. The course project is organized into a series of milestones, each of which is related to the corresponding module of the robot (e.g., joint motion, visual servoing, etc.). The students’ performance in the course project over the past three years has been summarized and reviewed. The evaluation results and the student feedback show that the proposed course project is interesting and engaging, and provides the students with a comprehensive opportunity to apply their theoretical knowledge in robotics, as well as to implement the findings of contemporary research in AI, to a real robotic system.